Performance of masonry buildings and churches in the 22 february 2011 christchurch earthquake

As part of the „Project Masonry‟ Recovery Project funded by the New Zealand Natural Hazards Research Platform, commencing in March 2011, an international team of researchers was deployed to document and interpret the observed earthquake damage to masonry buildings and to churches as a result of the 22nd February 2011 Christchurch earthquake. The study focused on investigating commonly encountered failure patterns and collapse mechanisms. A brief summary of activities undertaken is presented, detailing the observations that were made on the performance of and the deficiencies that contributed to the damage to approximately 650 inspected unreinforced clay brick masonry (URM) buildings, to 90 unreinforced stone masonry buildings, to 342 reinforced concrete masonry (RCM) buildings, to 112 churches in the Canterbury region, and to just under 1100 residential dwellings having external masonry veneer cladding. In addition, details are provided of retrofit techniques that were implemented within relevant Christchurch URM buildings prior to the 22nd February earthquake and brief suggestions are provided regarding appropriate seismic retrofit and remediation techniques for stone masonry buildings.The authors acknowledge the financial support for Project Masonry from the New Zealand Natural Hazards Research Platform. The testing of adhesive anchors was undertaken in conjunction with the RAPID grant CMMI-1138614 from the US National Science Foundation. The investigation of the performance of residential brick veneers was financially supported by Brickworks Building Products Australia

Shake‑table testing of a stone masonry building aggregate: overview of blind prediction study

City centres of Europe are often composed of unreinforced masonry structural aggregates, whose seismic response is challenging to predict. To advance the state of the art on the seismic response of these aggregates, the Adjacent Interacting Masonry Structures (AIMS) subproject from Horizon 2020 project Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe (SERA) provides shake-table test data of a two-unit, double-leaf stone masonry aggregate subjected to two horizontal components of dynamic excitation. A blind prediction was organized with participants from academia and industry to test modelling approaches and assumptions and to learn about the extent of uncertainty in modelling for such masonry aggregates. The participants were provided with the full set of material and geometrical data, construction details and original seismic input and asked to predict prior to the test the expected seismic response in terms of damage mechanisms, base-shear forces, and roof displacements. The modelling approaches used differ significantly in the level of detail and the modelling assumptions. This paper provides an overview of the adopted modelling approaches and their subsequent predictions. It further discusses the range of assumptions made when modelling masonry walls, floors and connections, and aims at discovering how the common solutions regarding modelling masonry in general, and masonry aggregates in particular, affect the results. The results are evaluated both in terms of damage mechanisms, base shear forces, displacements and interface openings in both directions, and then compared with the experimental results. The modelling approaches featuring Discrete Element Method (DEM) led to the best predictions in terms of displacements, while a submission using rigid block limit analysis led to the best prediction in terms of damage mechanisms. Large coefficients of variation of predicted displacements and general underestimation of displacements in comparison with experimental results, except for DEM models, highlight the need for further consensus building on suitable modelling assumptions for such masonry aggregates

Experimental and Numerical Assessment of Seismic Retrofit Solutions for Stone Masonry Buildings

This paper presents an experimental and numerical study on different retrofit solutions for stone masonry buildings with timber diaphragms in earthquake-prone regions, aiming at enhancing wall-to-diaphragm connections, diaphragms’ stiffness, and masonry properties. The experimental results of incremental dynamic shake-table tests on three full-scale two-story buildings, complemented by material and component characterization tests, are initially summarized. The first building specimen was unstrengthened. The second one was retrofitted at the floor and roof levels with improved wall-to-diaphragm connections and a moderate increase in diaphragm stiffness. Connections were also improved in the third specimen together with a significant enhancement of diaphragm stiffness. The calibration of two numerical models, versus the experimental response of the retrofitted building specimens, is then presented. The models were further modified and reanalyzed to assess the effects of masonry mechanical upgrades, which could be achieved in practice through deep joint repointing or various types of jacketing. These solutions were simulated by applying correction coefficients to the masonry mechanical properties, as suggested by the Italian building code. The effectiveness of the experimentally implemented and numerically simulated interventions are discussed in terms of strength enhancement and failure modes

Many-to-Many Metrics: A New Approach to Evaluate the Performance of Structural Damage Detection Networks

In the last years, Computer Vision and Deep Learning techniques have proved to be useful in supporting structural inspections of buildings and civil infrastructures. Particularly, in the case of post-disaster structural safety assessment, automated damage detection algorithms can accelerate the analysis of survey images and thus contribute to a fast screening of impacted areas. The identification of the various types of damage can be seen as a special case of object detection in which the goal is to identify sub-parts of a large object (e.g., cracks on a building). However, in this scenario, traditional evaluation metrics for object detection tend to underestimate the actual performance of the detector, since they considered a one-to-one match between a ground-truth box and a predicted box. Such approach could be sub-optimal for damage detection: for example, a crack can be labeled as a single entity in the ground truth but detected as two small cracks in inference or vice versa. To compensate this issue and better asses the performance of the detector, we introduce a new set of metrics called Many-to-Many. We tested these metrics using a YOLO network on two datasets containing images of damaged bridges and civil structures, and we collected evidence of an improved evaluation capability